which similar to Usefulness can account for varying preferences by
its \(\beta \)  parameter, although not gain. The \(F_{\beta }\) -score
assigns \(\beta \)  times as much importance to recall as to precision
(i.e., preference for completeness over exactness)[1]},
which is analogous to but not directly transferable to the \(\mu \) 
parameter in the Usefulness measure. While the \(F\) -score is commonly
seen to maximize completeness versus exactness of true positives,
the parameter can also be seen as a priority to minimize false negatives
versus false positives (FN prioritized over FP when \(\beta >1\) ). As
a heuristic, we map the balanced, standard \(F_{1}\) -score with \(\beta =1\) 
to \(U_{r}\)  with \(\mu =0.5\) , and match deviations from these preferences
according to \(\beta =\mu /(1-\mu )\) .
<TABLE>